Bristol-based Graphcore is Ready to Take on Nvidia and Intel

Bristol-based company Graphcore has raised $30 million (£23 million) in a Series A round, to complete the development of new processors that could dramatically speed up machine learning applications.

The round was led by Robert Bosch Venture Capital GmbH and Samsung Catalyst Fund; other backers included Amadeus Capital, C4 Ventures, Draper Esprit , Foundation Capital and Pitango Capital.

The company says its new Intelligence Processing Unit (IPU), together with its Poplar software framework, would be five to ten times faster than state-of-the-art processors at deep learning training — and even 50 times faster than current processors when it comes to more complex machine learning processes such as recurrent neural networks.

While avoiding to give away too many details on the technology, Graphcore CEO Nigel Toon explains that IPU’s architecture would be “multidimensional”, a feature that would give it a leg-up for machine learning tasks.

“The way people are training machine learning systems is by feeding minibatches of data in parallel to the GPU [Graphic processing units, the standard for machine learning training]. But for many networks —recurrent neural network, deep learning, reinforced learning—these kinds of structures are not well suited,” Toon says.

“You’ll actually need to feed data back inside the network, from the outside to the beginning on the network, so you can’t really feed a lot of data in parallel with GPU. Our processor is more complicated and much more parallel, and it can exploit parallelism in other dimensions.”

A graph generated by Graphcore’s software as it runs a machine learning model. The Bristol-based company says its new processor and software framework could speed up machine learning applications by about 50 times (via Graphcore)

In addition, Graphcore’s IPU would store the whole machine learning model inside the processor, which would dramatically increase its efficiency. Toon underlined how the technology can be used for a vast gamut of machine learning applications, and would work for both learning and inferencing.

The company, which is headquartered in Bristol but has offices in California, had been working for two years on its product before exiting stealth mode in the wake of last week’s funding round. It expects to release a commercial product by mid-2017.

The company is aiming high, hopeful that it might disrupt a machine learning environment currently defined and dominated by few incumbents.

“I think at the end of the day we’re going to wind up competing with Intel and Nvidia,” Toon says.”That’s certainly our goal.”